Performance Prediction of Power Amplifiers for the Extended Bandwidth via Neural Networks

Abstract This paper presents the optimization methodology for modeling the power amplifier (PA) with the aid of deep neural network (DNN). In this paper we propose an impressive approach leading to extrapolate frequency responses of the PA, where the long short-term memory (LSTM) DNN is employed. The presented method models the PA accurately in terms of scattering parameters, gain, output power and efficiency. This approach tackles the problem of dependency to the engineer experience and reduces the challenges in achieving large frequency band. All the modeling process is performed with the combination of electronic design automation tool and numerical analyzer where automated environment is created. For validating the proposed method, one PA is designed and modelled for the range frequency of 1 to 2.3 GHz. The DNN is firstly trained for the half of the bandwidth and later, the modeled PA is used for predicting the extended frequency band.